An Efficient Sound and Data Steganography Based Secure Authentication System

The prodigious advancements in contemporary technologies have also brought in the situation of unprecedented cyber-attacks. Further, the pin-based security system is an inadequate mechanism for handling such a scenario. The reason is that hackers use multiple strategies for evading security systems and thereby gaining access to private data. This research proposes to deploy diverse approaches for authenticating and securing a connection amongst two devices/gadgets via sound, thereby disregarding the pins’ manual verification. Further, the results demonstrate that the proposed approaches outperform conventional pin-based authentication or QR authentication approaches. Firstly, a random signal is encrypted, and then it is transformed into a wave file, after which it gets transmitted in a short burst via the device’s speakers. Subsequently, the other device/gadget captures these audio bursts through its microphone and decrypts the audio signal for getting the essential data for pairing. Besides, this model requires two devices/gadgets with speakers and a microphone, and no extra hardware such as a camera, for reading the QR code is required. The first module is tested with real-time data and generates high scores for the widely accepted accuracy metrics, including precision, Recall, F1 score, entropy, and mutual information (MI). Additionally, this work also proposes a module helps in a secured transmission of sensitive data by encrypting it over images and other files. This steganographic module includes two-stage encryption with two different encryption algorithms to transmit data by embedding inside a file. Several encryption algorithms and their combinations are taken for this system to compare the resultant file size. Both these systems engender high accuracies and provide secure connectivity, leading to a sustainable communication ecosystem.     

Machine Learning and Deep Learning powered satellite communications: Enabling technologies,applications, open challenges,and future research directions

The recent wave of creating an interconnected world through satellites has renewed interest in satellite communications. Private and government-funded space agencies are making advancements in the creation of satellite constellations, and the introduction of 5G has brought a new focus to a fully connected world. Satellites are the proposed solutions for establishing high throughput and low latency links to remote, hard-to-reach areas. This has caused the injection of many satellites in Earth's orbit, which has caused many discrepancies. There is a need to establish highly adaptive and flexible satellite systems to overcome this. Machine Learning (ML) and Deep Learning (DL) have gained much popularity when it comes to communication systems. This review extensively provides insight into ML and DL's utilization in satellite communications. This review covers how satellite communication subsystems and other satellite system applications can be implemented through Artificial Intelligence (AI) and the ongoing open challenges and future directions.     

Strength design of composite beam using gradient and particle swarm optimization

This work addresses the optimum design of a composite box-beam structure subject to strength constraints. Such box-beams are used as the main load carrying members of helicopter rotor blades. A computationally efficient analytical model for box-beam is used. Optimal ply orientation angles are sought which maximize the failure margins with respect to the applied loading. The Tsai–Wu–Hahn failure criterion is used to calculate the reserve factor for each wall and ply and the minimum reserve factor is maximized. Ply angles are used as design variables and various cases of initial starting design and loadings are investigated. Both gradient-based and particle swarm optimization (PSO) methods are used. It is found that the optimization approach leads to the design of a box-beam with greatly improved reserve factors which can be useful for helicopter rotor structures. While the PSO yields globally best designs, the gradient-based method can also be used with appropriate starting designs to obtain useful designs efficiently.     

Preparation and pool boiling characteristics of copper nanofluids over a flat plate heater

The copper nanoparticles of average size of 10 nm have been prepared by the sputtering method and characterized through atomic force microscopy (AFM), X-ray diffraction (XRD) and transmission electron microscopy (TEM). The pool boiling heat transfer characteristics of 0.25%, 0.5% and 1.0% by weight concentrations of copper nanoparticles has been studied. Different copper based nanofluids were prepared in both, distilled water and distilled water with 9.0 wt% of sodium lauryl sulphate anionic surfactant (SDS). The pool boiling heat transfer data were acquired for the boiling of nanofluids over a 30 mm square and 0.44 mm thick stainless steel plate heater. The experimental results show that for the critical heat flux of pure water is 80% higher than that of water–surfactant fluid. Also, it was found that the critical heat flux for 0.25%, 0.5% and 1.0% concentrations of copper nanoparticles in copper–water nanofluids are 25%, 40% and 48% higher than that of pure water. But in the case of copper–water with surfactant nanofluids comparing with pure water, the CHF decreases to 75%, 68%, and 62% for respective concentrations of copper nanoparticles. The heat transfer coefficient decreases with increase of nanoparticles concentration in both water–copper and water–copper with surfactant nanofluids.     

Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

Cloud data centers consume high volume of energy for processing and switching the servers among different modes. Virtual Machine (VM) migration enhances the performance of cloud servers in terms of energy efficiency, internal failures and availability. On the other end, energy utilization can be minimized by decreasing the number of active, underutilized sources which conversely reduces the dependability of the system. In VM migration process, the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the operations. In this view, the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization (IMFP-VMMO) model in cloud environment. The major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure prediction. To accomplish this, IMFP-VMMO model employs Gradient Boosting Decision Tree (GBDT) classification model at initial stage for effectual prediction of VM failures. At the same time, VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm (QO-AFSA) which in turn reduces the energy consumption. The performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed model. The comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.     

Enhanced Deep Autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System

In the era of Big data, learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system (IDS). Owing to the lack of accurately labeled network traffic data, many unsupervised feature representation learning models have been proposed with state-of-the-art performance. Yet, these models fail to consider the classification error while learning the feature representation. Intuitively, the learnt feature representation may degrade the performance of the classification task. For the first time in the field of intrusion detection, this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder (DAE) for learning the robust feature representation and one-class support vector machine (OCSVM) for finding the more compact decision hyperplane for intrusion detection. Specially, the proposed model defines a new unified objective function to minimize the reconstruction and classification error simultaneously. This unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion detection. Three set of evaluation experiments are conducted to demonstrate the potential of the proposed model. First, the ablation evaluation on benchmark dataset, NSL-KDD validates the design decision of the proposed model. Next, the performance evaluation on recent intrusion dataset, UNSW-NB15 signifies the stable performance of the proposed model. Finally, the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.     

Intelligent Feature Selection with Deep Learning Based Financial Risk Assessment Model

Due to global financial crisis, risk management has received significant attention to avoid loss and maximize profit in any business. Since the financial crisis prediction (FCP) process is mainly based on data driven decision making and intelligent models, artificial intelligence (AI) and machine learning (ML) models are widely utilized. This article introduces an intelligent feature selection with deep learning based financial risk assessment model (IFSDL-FRA). The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or enterprise. In addition, the IFSDL-FRA technique involves the design of new water strider optimization algorithm based feature selection (WSOA-FS) manner to an optimum selection of feature subsets. Moreover, Deep Random Vector Functional Link network (DRVFLN) classification technique was applied to properly allot the class labels to the financial data. Furthermore, improved fruit fly optimization algorithm (IFFOA) based hyperparameter tuning process is carried out to optimally tune the hyperparameters of the DRVFLN model. For enhancing the better performance of the IFSDL-FRA technique, an extensive set of simulations are implemented on benchmark financial datasets and the obtained outcomes determine the betterment of IFSDL-FRA technique on the recent state of art approaches.     

Delineating the effect of substituent and π-bridge flip on the photophysical properties of pyrene derivatives: answers from DFT/TD-DFT calculations

Journal of Materials Science - Pyrene-based Schiff base derivatives have attracted enormous potential as fluorescent probe for multifarious research applications due to their fashionable...     

Experimental and artificial intelligence for determination of stable criteria in cyclic voltammetric process of medicinal herbs for biofuel cells

This study proposed an expert system approach on the basis of artificial intelligence (AI) in the modeling of cyclic voltammogram (CV) profiles of green tea extracts. AI approach of artificial neural networks is applied to generate the model phase‐plane portraits of current output versus applied voltage through CV scan cycles. The predicted current values were validated using experiments, and generic ability of approach was examined by testing on the CV scan cycles generated from Syzygium aromaticum and Citrus reticulate. It was concluded that AI approach can be employed to reveal stable point (cycle and voltage) in CV profiles for bioenergy applications.